{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys,copy,os,inspect\n",
    "if hasattr(sys.modules[__name__], '__file__'):\n",
    "    _file_name = __file__\n",
    "else:\n",
    "    _file_name = inspect.getfile(inspect.currentframe())\n",
    "CURRENT_FILE_PATH = os.path.dirname(_file_name)\n",
    "sys.path.append(os.getcwd()+\"/../neuronVis\")\n",
    "import nrrd\n",
    "import trimesh\n",
    "import IONData\n",
    "from turtle import rt\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from PIL import Image\n",
    "from scipy.ndimage import binary_dilation\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.use('module://matplotlib_inline.backend_inline')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7088.73 4201.35 2847.6 ]\n",
      " [7089.83 4193.6  2848.33]\n",
      " [7092.34 4205.65 2848.49]\n",
      " ...\n",
      " [5933.79 5033.28 5092.03]\n",
      " [5924.73 5024.71 5092.95]\n",
      " [5928.58 5029.03 5094.54]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "iondata = IONData.IONData()\n",
    "res,path = iondata.getStructureMask(262)\n",
    "rtstructureMask,header = nrrd.read(path)\n",
    "part1mesh = trimesh.load('../resource/rt/262_part_1.1.obj')\n",
    "part2mesh = trimesh.load('../resource/rt/262_part_2.1.obj')\n",
    "\n",
    "print(part1mesh.vertices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1320, 800, 1140)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print(rtstructureMask.shape)\n",
    "mask = np.zeros(rtstructureMask.shape)\n",
    "plt.figure(figsize=(20, 20)) \n",
    "for v in part1mesh.vertices:\n",
    "    # print(int(v[0]/10),int(v[1]/10),int(v[2]/10))\n",
    "    mask[int(v[0]/10),int(v[1]/10),int(v[2]/10)]=10\n",
    "plt.imshow(mask[:,:,360].T)\n",
    "mask= mask.astype(np.int8)\n",
    "nrrd.write('../resource/rt/part1.nrrd',mask)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iondata = IONData.IONData()\n",
    "res,path = iondata.getStructureMask(262)\n",
    "rtstructureMask,header = nrrd.read(path)\n",
    "rtstructureMask2 = binary_dilation(rtstructureMask,None,iterations=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20)) \n",
    "plt.imshow(rtstructureMask2[:,:,360].T)\n",
    "rtstructureMask2.dtype\n",
    "rtstructureMask2=rtstructureMask2.astype(np.int8)\n",
    "rtstructureMask2 = rtstructureMask2*10\n",
    "nrrd.write('../resource/rt/rtDilationMask1.nrrd',rtstructureMask2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a9d0e43cd0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20)) \n",
    "plt.imshow(rtstructureMask[:,:,360].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rtboundaryMask,header = nrrd.read('../resource/rt/rtboundary.nrrd')\n",
    "rtpart1Mask,header = nrrd.read('../resource/rt/part1.nrrd')\n",
    "rtpart2Mask,header = nrrd.read('../resource/rt/part2.nrrd')\n",
    "rtpart1Mask = binary_dilation(rtpart1Mask,None,iterations=2)\n",
    "rtpart2Mask = binary_dilation(rtpart2Mask,None,iterations=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "rtpart1Mask.astype(np.int8)\n",
    "rtpart2Mask.astype(np.int8)\n",
    "rtpart1Mask = rtpart1Mask*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f836fd3e50>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rtpart1Mask[rtpart2Mask==1]=1\n",
    "rtpart1Mask[rtboundaryMask==2]=5\n",
    "rtpart1Mask=rtpart1Mask.astype(np.int8)\n",
    "plt.figure(figsize=(20, 20)) \n",
    "plt.imshow(rtpart1Mask[:,:,360].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rtpart1Mask=rtpart1Mask.astype(np.int8)\n",
    "rtpart1Mask[rtpart1Mask==0]=10\n",
    "rtpart1Mask[rtpart1Mask==1]=0\n",
    "rtpart1Mask.dtype\n",
    "nrrd.write('../resource/rt/test1.nrrd',rtpart1Mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'rtpart1Mask' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32md:\\project\\python\\neuron-vis\\figures\\rtflatmap.ipynb Cell 8\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/project/python/neuron-vis/figures/rtflatmap.ipynb#X16sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m rtpart1Mask\n",
      "\u001b[1;31mNameError\u001b[0m: name 'rtpart1Mask' is not defined"
     ]
    }
   ],
   "source": [
    "rtpart1Mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "rtboundaryMask20,header = nrrd.read('../resource/rt/rtboundary-20.nrrd')\n",
    "nrrd.write('../resource/rt/rtboundary-20.nrrd',rtboundaryMask20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rtboundaryMask10,header = nrrd.read('../resource/rt/rtboundary-10.nrrd')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b256171b80>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "shape = (rtboundaryMask10.shape)\n",
    "boundary=np.zeros(shape)\n",
    "for k in range(286,513):\n",
    "# for k in range(359,360):\n",
    "    for j in range(325,534):\n",
    "        for i in range(570,747):\n",
    "            if rtboundaryMask10[i,j,k]!=5:\n",
    "                if \\\n",
    "                    rtboundaryMask10[i+1,j,k]==5 or \\\n",
    "                    rtboundaryMask10[i-1,j,k]==5 or \\\n",
    "                    rtboundaryMask10[i,j+1,k]==5 or \\\n",
    "                    rtboundaryMask10[i,j-1,k]==5 or \\\n",
    "                    rtboundaryMask10[i,j,k+1]==5 or \\\n",
    "                    rtboundaryMask10[i,j,k-1]==5:\n",
    "                    boundary[i,j,k]=rtboundaryMask10[i,j,k]/10\n",
    "plt.figure(figsize=(20, 20)) \n",
    "plt.imshow(boundary[:,:,359].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "boundary.dtype\n",
    "boundary=boundary.astype(np.int8)\n",
    "nrrd.write('../resource/rt/rtboundary10.nrrd',boundary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "shape = (rtboundaryMask10.shape)\n",
    "rtmask=np.zeros(shape)\n",
    "rtmask = rtmask.astype(np.int8)\n",
    "rtmask[rtboundaryMask10==5]=5\n",
    "nrrd.write('../resource/rt/rtmask.nrrd',rtmask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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